Apache Flink® is an open-source stream processing framework for distributed and accurate data streaming applications. An increasing number of IoT use cases will (and some already do) require robust processing frameworks that can handle an ever-increasing amount of data and provide insights in real time. Apache Flink is one of the contenders for the top spot among such frameworks and in this presentation Aljoscha Krettek will highlight some of the properties that make Flink so well suited for IoT use cases: We will first learn what stream processing frameworks in general provide before diving into stateful stream processing and event-time based stream-processing. We will see why these two features are important for IoT scenarios and also why they, together with Flink’s robust handling of failures, enable accurate and robust analytics on real-time streaming data.

27.
Savepoints
§ A persistent snapshot of all state
§ When starting an application, state can
be initialized from a savepoint
§ In-between savepoint and restore we can
update Flink version or user code
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35.
What is Event-Time Streaming
§ Events have timestamps
§ Processing depends on
timestamps
§ An event-time stream
processor should give you the
tools to reason about time
• Handle streams that are out of
order
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Your
code
state
t3 t1 t2t4 t1-t2 t3-t4

36.
Recap: Event-Time
§ IoT use cases need event-time
processing
§ Even small mismatch of event
time/processing time will lead to wrong
results
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